Testing Statistical Hypotheses (Springer Texts in Statistics) [Hardcover]

E. L. Lehmann (Author), Springer (Author)

Book Description

ISBN-10: 0387949194 | ISBN-13: 978-0387949192 | Publication Date: January 27, 1997 | Edition: 2nd

This classic textbook, now available from Springer, summarizes developments in the field of hypotheses testing. Optimality considerations continue to provide the organizing principle. However, they are now tempered by a much stronger emphasis on the robustness properties of the resulting procedures. This book is an essential reference for any graduate student in statistics.

Editorial Reviews

Review

No professional mathematical statician should be without this book; no teacher of statistical theory can afford to be.
-Journal of the Royal Statistical Society --This text refers to an out of print or unavailable edition of this title.

From the Back Cover

The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760. E.L. Lehmann is Professor of Statistics Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands and the University of Chicago. He is the author of Elements of Large-Sample Theory and (with George Casella) he is also the author of Theory of Point Estimation, Second Edition. Joseph P. Romano is Professor of Statistics at Stanford University. He is a recipient of a Presidential Young Investigator Award and a Fellow of the Institute of Mathematical Statistics. He has coauthored two other books, Subsampling with Dimitris Politis and Michael Wolf, and Counterexamples in Probability and Statistics with Andrew Siegel. --This text refers to an alternate Hardcover edition.

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Product Details

• Hardcover: 600 pages
• Publisher: Springer; 2nd edition (January 27, 1997)
• Language: English
• ISBN-10: 0387949194
• ISBN-13: 978-0387949192
• Product Dimensions: 9.6 x 6.5 x 1.5 inches
• Shipping Weight: 2.6 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (6 customer reviews)
• Amazon Best Sellers Rank: #2,495,173 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

36 of 37 people found the following review helpful:

5.0 out of 5 stars classic text with new publisher, February 12, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
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This review is from: Testing Statistical Hypotheses (Springer Texts in Statistics) (Hardcover)

This text was commonly used as a graduate text in mathematical statistics in the 1970s when I was a graduate student at Stanford University. It was the best and most detailed text on the theory of hypothesis testing. Over the years it remained so and twenty years after publication, when it was outdated by research advances it was revised by Professor Lehmann. The second edition originally published by Wiley went out of print but has now been reprinted by Springer-Verlag. This is a great book for any statistician to have on his bookshelf, a must have reference!

4 of 5 people found the following review helpful:

5.0 out of 5 stars 3rd edition has lots of new material, February 10, 2008

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R. D. Rivers (Palo Alto, CA USA) - See all my reviews
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This review is from: Testing Statistical Hypotheses (Springer Texts in Statistics) (Hardcover)

The 3rd edition has an entirely new set of chapters covering asymptotics. I found this to be a very readable survey, including a good discussion of local asymptotic normality, which is not treated in more elementary texts. There's some overlap between this book and Lehman's Theory of Point Estimation. It's not obvious which should be read first, but both books are very well written with many interesting problems.

6 of 9 people found the following review helpful:

3.0 out of 5 stars A few details are not quite right, October 24, 2009

By 

Harold M. Kaplan (Annapolis, MD, USA) - See all my reviews
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This review is from: Testing Statistical Hypotheses (Springer Texts in Statistics) (Hardcover)

Lehmann and Romano, Third Edition, fourth printing, 2008, is a wonderful, beautiful, necessary book for the shelf of every serious statistician, but in a few ways it is not quite right. Some important topics are omitted. At least one important topic is much more important than the book says. At least one statement, while correct, may be read incorrectly by beginners. At least one proof is unreadable.

An omission is heteroskedasticity. The usual tests for 2-samples and k-samples are wrong in its presence. The same is true for the usual test for blocks and treatments, but there exists an exact Monte Carlo test for blocks and treatments which works correctly in the presence of heteroskedasticity. Another omission is Doob's inequality for nonnegative martingales, which connects up some Bayes tests with some frequentist tests.

Simpson's paradox (page 132 bottom) is treated at length in the book, but the treatment does not suffice, and there might not be any treatment which could suffice. The paradox strikes at nearly all of what statisticians do. The book ought to use big bold-face type for the statement of the paradox. Also, the book ought to include an example, not just give a reference.

The account of Monte Carlo tests (page 442) may seem to suggest that Monte Carlo gives only an approximation and that its accuracy depends on how many random numbers are used. The reader is not told that Monte Carlo tests are commonly exact tests for small samples. (And where in the book is the word "exact"?)

On page 353 I am unable to follow the (very short) proof of Theorem 9.1.3. The complexity of the notation is perhaps responsible.